Establishment of a novel signature to predict prognosis and immune characteristics of pancreatic cancer based on necroptosis-related long non-coding RNA
Background Necroptosis plays an important role in tumorigenesis and tumour progression. Long noncoding RNAs (lncRNAs) have been proven to be regulatory factors of necroptosis in various tumours. However, the real role of necroptosis-related lncRNAs (NRLs) and their potential to predict the prognosis of pancreatic cancer (PC) remain largely unclear. The goal of this study was to identify NRLs and create a predictive risk signature in PC, explore its prognostic predictive performance, and further assess immunotherapy and chemotherapy responses. Methods RNA sequencing data, tumour mutation burden (TMB) data, and clinical profiles of 178 PC patients were downloaded from The Cancer Genome Atlas (TCGA) database. NRLs were identified using Pearson correlation analysis. Then, patients were divided into the training set and the validation set at a 1:1 ratio. Subsequently, Cox and LASSO regression analyses were conducted to establish a prognostic NRL signature in the training set and validation set. The predictive efficacy of the 5-NRL signature was assessed by survival analysis, nomogram, Cox regression, clinicopathological feature correlation analysis, and receiver operating characteristic (ROC) curve analysis. Furthermore, correlations between the risk score (RS) and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anticancer drug sensitivity were analysed. Finally, we used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to validate the 5-NRLs. Results A 5-NRL signature was established to predict the prognosis of PC, including LINC00857, AL672291.1, PTPRN2-AS1, AC141930.2, and MEG9. The 5-NRL signature demonstrated a high degree of predictive power according to ROC and Kaplan‒Meier curves and was revealed to be an independent prognostic risk factor via stratified survival analysis. Nomogram and calibration curves indicated the clinical adaptability of the signature. Immune-related pathways were linked to the 5-NRL signature according to enrichment analysis. Additionally, immune cell infiltration, immune checkpoint molecules, somatic gene mutations and the half-maximal inhibitory concentration (IC50) of chemotherapeutic agents were significantly different between the two risk subgroups. These results suggested that our model can be used to evaluate the effectiveness of immunotherapy and chemotherapy, providing a potential new strategy for treating PC. Conclusions The novel 5-NRL signature is helpful for assessing the prognosis of PC patients and improving therapy options, so it can be further applied clinically..
| Medienart: |
Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:50 |
|---|---|
| Enthalten in: |
Molecular biology reports - 50(2023), 9 vom: 15. Juli, Seite 7405-7419 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Xiong, Yuanpeng [VerfasserIn] |
|---|
| Links: |
Volltext [lizenzpflichtig] |
|---|
| BKL: | |
|---|---|
| Themen: |
Chemotherapy |
| Anmerkungen: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
|---|
| doi: |
10.1007/s11033-023-08663-3 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
OLC2145249648 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2145249648 | ||
| 003 | DE-627 | ||
| 005 | 20240118103529.0 | ||
| 007 | tu | ||
| 008 | 240118s2023 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11033-023-08663-3 |2 doi | |
| 035 | |a (DE-627)OLC2145249648 | ||
| 035 | |a (DE-He213)s11033-023-08663-3-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 570 |q VZ |
| 084 | |a 12 |2 ssgn | ||
| 084 | |a BIODIV |q DE-30 |2 fid | ||
| 084 | |a 42.00 |2 bkl | ||
| 100 | 1 | |a Xiong, Yuanpeng |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Establishment of a novel signature to predict prognosis and immune characteristics of pancreatic cancer based on necroptosis-related long non-coding RNA |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. | ||
| 520 | |a Background Necroptosis plays an important role in tumorigenesis and tumour progression. Long noncoding RNAs (lncRNAs) have been proven to be regulatory factors of necroptosis in various tumours. However, the real role of necroptosis-related lncRNAs (NRLs) and their potential to predict the prognosis of pancreatic cancer (PC) remain largely unclear. The goal of this study was to identify NRLs and create a predictive risk signature in PC, explore its prognostic predictive performance, and further assess immunotherapy and chemotherapy responses. Methods RNA sequencing data, tumour mutation burden (TMB) data, and clinical profiles of 178 PC patients were downloaded from The Cancer Genome Atlas (TCGA) database. NRLs were identified using Pearson correlation analysis. Then, patients were divided into the training set and the validation set at a 1:1 ratio. Subsequently, Cox and LASSO regression analyses were conducted to establish a prognostic NRL signature in the training set and validation set. The predictive efficacy of the 5-NRL signature was assessed by survival analysis, nomogram, Cox regression, clinicopathological feature correlation analysis, and receiver operating characteristic (ROC) curve analysis. Furthermore, correlations between the risk score (RS) and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anticancer drug sensitivity were analysed. Finally, we used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to validate the 5-NRLs. Results A 5-NRL signature was established to predict the prognosis of PC, including LINC00857, AL672291.1, PTPRN2-AS1, AC141930.2, and MEG9. The 5-NRL signature demonstrated a high degree of predictive power according to ROC and Kaplan‒Meier curves and was revealed to be an independent prognostic risk factor via stratified survival analysis. Nomogram and calibration curves indicated the clinical adaptability of the signature. Immune-related pathways were linked to the 5-NRL signature according to enrichment analysis. Additionally, immune cell infiltration, immune checkpoint molecules, somatic gene mutations and the half-maximal inhibitory concentration (IC50) of chemotherapeutic agents were significantly different between the two risk subgroups. These results suggested that our model can be used to evaluate the effectiveness of immunotherapy and chemotherapy, providing a potential new strategy for treating PC. Conclusions The novel 5-NRL signature is helpful for assessing the prognosis of PC patients and improving therapy options, so it can be further applied clinically. | ||
| 650 | 4 | |a Necroptosis | |
| 650 | 4 | |a lncRNA | |
| 650 | 4 | |a Pancreatic cancer | |
| 650 | 4 | |a Immunotherapy | |
| 650 | 4 | |a Chemotherapy | |
| 650 | 4 | |a Survival analysis | |
| 700 | 1 | |a Kong, Xiaoyu |4 aut | |
| 700 | 1 | |a Fang, Kang |4 aut | |
| 700 | 1 | |a Sun, Gen |4 aut | |
| 700 | 1 | |a Tu, Shuju |4 aut | |
| 700 | 1 | |a Wei, Yongyang |4 aut | |
| 700 | 1 | |a Ouyang, Yonghao |4 aut | |
| 700 | 1 | |a Wan, Renhua |4 aut | |
| 700 | 1 | |a Xiao, Weidong |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Molecular biology reports |d Springer Netherlands, 1960 |g 50(2023), 9 vom: 15. Juli, Seite 7405-7419 |w (DE-627)129400998 |w (DE-600)186544-4 |w (DE-576)014783312 |x 0301-4851 |7 nnns |
| 773 | 1 | 8 | |g volume:50 |g year:2023 |g number:9 |g day:15 |g month:07 |g pages:7405-7419 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11033-023-08663-3 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a FID-BIODIV | ||
| 912 | |a SSG-OLC-CHE | ||
| 936 | b | k | |a 42.00 |q VZ |
| 951 | |a AR | ||
| 952 | |d 50 |j 2023 |e 9 |b 15 |c 07 |h 7405-7419 | ||